Multiresponse Parameter Estimation for Finite-Element Model Updating Using Nondestructive Test Data

Structural health monitoring using field measurements has developed into a major research area, responding to an increasing demand for evaluating the integrity of civil engineering structures. Model updating through parameter estimation is a key tool in a successful structural health monitoring program. A method for parameter estimation is developed for simultaneous use of static and modal nondestructive test data called the “multiresponse” parameter estimation. An error function normalization technique is also developed to facilitate effective multiresponse parameter estimation. This normalization technique can mitigate some of the numerical issues encountered during the parameter estimation procedure. However, this technique does not degrade the integrity of the parameter estimation procedure. Multiresponse parameter estimation provides an increased level of flexibility and feasibility of model updating for structural health monitoring. This paper presents full integration of static and modal nondestructive test data using both stiffness-based and mass-based error functions for structural health monitoring. A benchmark laboratory grid model of a bridge deck is utilized to illustrate application of both normalization and multiresponse parameter estimation for updating the stiffness and mass parameters using nondestructive test data.

[1]  James L. Beck,et al.  Two-Stage Structural Health Monitoring Approach for Phase I Benchmark Studies , 2004 .

[2]  Z. Yao,et al.  Structural damage identification using static test data and changes in frequencies , 2001 .

[3]  Shijun Guo IMPROVEMENT OF A TAIL-PLANE STRUCTURAL MODEL USING VIBRATION TEST DATA , 2002 .

[4]  H. Lee,et al.  STATISTICAL DAMAGE ASSESSMENT OF FRAMED STRUCTURES FROM STATIC RESPONSES , 2000 .

[5]  A. Emin Aktan,et al.  Structural identification of phenomenological physical models with controlled mechanisms of uncertainty , 2001, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[6]  Chung Bang Yun,et al.  Neural networks-based damage detection for bridges considering errors in baseline finite element models , 2003 .

[7]  Samir N. Shoukry,et al.  Remote Health Monitoring and Modeling of Star City Bridge, West Virginia , 2006 .

[8]  Raimondo Betti,et al.  Identification of Structural Systems using an Evolutionary Strategy , 2004 .

[9]  Keith D. Hjelmstad,et al.  Damage detection and assessment of structures from static response , 1997 .

[10]  Darryll J. Pines,et al.  Constrained Damage Detection Technique for Simultaneously Updating Mass and Stiffness Matrices , 1998 .

[11]  John B. Mander,et al.  A simple LMS‐based approach to the structural health monitoring benchmark problem , 2005 .

[12]  Masoud Sanayei,et al.  Using multi-response parameter estimation for structural condition and risk assessment of in-service bridges , 2005 .

[13]  Nicholas A J Lieven,et al.  Damage Prognosis: Current Status and Future Needs , 2003 .

[14]  Charles R. Farrar,et al.  Comparative study of damage identification algorithms applied to a bridge: I. Experiment , 1998 .

[15]  A. Emin Aktan,et al.  Parameter Estimation for Multiple-Input Multiple-Output Modal Analysis of Large Structures , 2004 .

[16]  Benjamin A. Graybeal,et al.  STUDYING THE RELIABILITY OF BRIDGE INSPECTION , 2000 .

[17]  Eli Cuelho,et al.  Evaluating Concrete Bridge Deck Performance Using Active Instrumentation , 2006 .

[18]  Arthur J. Helmicki,et al.  Structural Identification for Condition Assessment: Experimental Arts , 1997 .

[19]  G. De Roeck,et al.  System identification of mechanical structures by a high-order multivariate autoregressive model , 1997 .

[20]  Masoud Sanayei,et al.  Structural Parameter Estimation Using Modal Responses and Utilizing Genetic Algorithm , 2000 .

[21]  Masoud Sanayei,et al.  Damage assessment of structures using static test data , 1991 .

[22]  Jamshid Ghaboussi,et al.  Genetic algorithm in structural damage detection , 2001 .

[23]  Raphael T. Haftka,et al.  Structural optimization complexity: what has Moore’s law done for us? , 2004 .

[24]  O. Nelles,et al.  An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.

[25]  Masoud Sanayei,et al.  STRUCTURAL MODEL UPDATING USING EXPERIMENTAL STATIC MEASUREMENTS , 1997 .

[26]  Jeong‐Tae Kim,et al.  Improved damage identification method based on modal information , 2002 .

[27]  Masoud Sanayei,et al.  Selection of noisy measurement locations for error reduction in static parameter identification , 1992 .

[28]  Masoud Sanayei,et al.  PARAMETER ESTIMATION INCORPORATING MODAL DATA AND BOUNDARY CONDITIONS , 1999 .

[29]  Ahmet E. Aktan,et al.  ISSUES IN INFRASTRUCTURE HEALTH MONITORING FOR MANAGEMENT , 2000 .

[30]  Lee D. Peterson,et al.  Experimental Determination of Local Structural Stiffness by Disassembly of Measured Flexibility Matrices , 1995 .

[31]  Byung Hwan Oh,et al.  STRUCTURAL DAMAGE ASSESSMENT WITH COMBINED DATA OF STATIC AND MODAL TESTS , 1998 .

[32]  Ning Hu,et al.  Structural damage identification using static test data and changes in frequencies , 2000 .

[33]  Sung-Pil Chang,et al.  Experimental Investigation of System-Identification-Based Damage Assessment on Structures , 2002 .

[34]  Chan Ghee Koh,et al.  PARAMETER IDENTIFICATION OF LARGE STRUCTURAL SYSTEMS IN TIME DOMAIN , 2000 .

[35]  Masoud Sanayei,et al.  Significance of Modeling Error in Structural Parameter Estimation , 2001 .

[36]  Richard B. Nelson,et al.  Identification of Structural Element Stiffnesses from Incomplete Static Test Data , 1986 .